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检索条件"任意字段=2023 International Conference on Algorithms, Computing and Data Processing, ACDP 2023"

共 6952 条记录，以下是171-180 订阅

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Optimization of dedicated domain text classification based on data augmentation using BERT generation pre-trained model

Optimization of dedicated domain text classification based o...

引用

2023 international conference on algorithms, High Performance computing, and Artificial Intelligence, AHPCAI 2023

作者： Xiang, Yuzhe Cui, Can Zhu, Xinjie Wang, Zhi North China Institute of Computing Technology Platform R&D Center China

ISBN: (纸本)9781510671881

Due to the excessive cost of data collection as well as annotation in dedicated domains, artificial intelligence model training is difficult to achieve optimality with insufficient data. To optimize this issue, a text generation data augmentation method based on the BERT model is proposed in this paper for augmenting the small amount of available annotated data. The optimization of this data augmentation method is demonstrated by experiments. In a text classification experiment, this data augmentation method can improve the training effect of the source data by 2.9%. © 2023 SPIE. All rights reserved.

关键词： Classification (of information)

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Research on Hybrid Homomorphic Encryption Schemes Based on Paillier and ElGamal Encryption algorithms 24

Research on Hybrid Homomorphic Encryption Schemes Based on P...

引用

4th international conference on Artificial Intelligence, Big data and algorithms, CAIBDA 2024

作者： Ge, Yunhan Chen, Bing The Third Research Institute The Ministry of Public Security Shanghai China

ISBN: (纸本)9798400710247

With the continuous development and widespread application of big data and cloud computing technologies, there is an urgent need to balance the security and convenience of data during its use and storage. To enable secure computation and usage of data in its encrypted state, this study conducts an in-depth analysis of the encryption principles and homomorphic properties of two partial homomorphic encryption techniques, namely the Paillier algorithm and the ElGamal algorithm. Leveraging the additive homomorphic operation property of the Paillier algorithm and the multiplicative homomorphic operation property of the ElGamal algorithm, these two algorithms have been integrated to establish a hybrid homomorphic encryption scheme (HHE). Compared with existing similar technical solutions, this scheme simultaneously satisfies the requirements for both addition and multiplication operations. In addition, it enhances security by the integration of reliable homomorphic encryption algorithms. Through empirical computation, the feasibility of the HHE scheme's encryption has been validated, as well as its security has been analyzed. Therefore, it achieves secure and reliable computation and processing of encrypted data. This research contributes to the ongoing efforts to enhance data security in the era of big data and cloud computing, providing a practical solution for secure data computation and usage. © 2024 Copyright held by the owner/author(s).

关键词： Big data Encryption

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SDADS: Stream data Anomaly Detection System 2

SDADS: Stream Data Anomaly Detection System

引用

2nd international conference on Cloud computing, Big data Application and Software Engineering, CBASE 2023

作者： Wang, Yusheng Zhang, Anyi Beijing University of Posts and Telecommunications State Key Lab of Networking and Switching Technology Beijing China

ISBN: (纸本)9798350331448

In a cloud-native architecture, the operational data of various system components experiences a significant increase. From the distributed complex system, obtaining the operation status data and realizing real-time monitoring and abnormal alarm play an important role in guaranteeing the smooth production. However, handling a large volume of stream data in real-time poses challenges such as high computational demands, low latency, and high concurrency. Therefore, this study presents the design and implementation of a system for anomaly detection in stream data called Stream data Anomaly Detection System (SDADS). SDADS leverages message queues for processing business data and further processes the data using a distributed computing framework. It also provides functions for persistent data storage and anomaly detection alerts. The use of SDADS enables the management of complex business processes such as server provisioning and application deployment, simplifies business development logic, reduces operational time cost, and allows users to focus solely on the anomaly detection algorithms themselves. © 2023 IEEE.

关键词： Anomaly detection

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Prediction models for Clustered Virtual Machines in data Centers 18

Prediction models for Clustered Virtual Machines in Data Cen...

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18th international conference on Future Networks and Communications (FNC) / 20th international conference on Mobile Systems and Pervasive computing (MobiSPC) / 13th international conference on Sustainable Energy Information Technology (SEIT)

作者： Estrada, Rebeca Cordova-Garcia, Jose Vera, Nelson Escuela Super Politecn Litoral Km 30-5 Via Perimetral Guayaquil Ecuador

In data center (DC) environments, machine learning algorithms play an important role in resource management to increase efficiency by means of proper predictive monitoring workload trends and adjusting jobs accordingly. In this paper, we propose a system to predict the CPU usage of virtual machines (VMs) of a DC. Our proposal performs clustering of VMs based on their historical information (i.e., time series) by evaluating several traditional ML algorithms using common statistical features of VM time series, which facilitates grouping VMs with similar behaviors and establishing clusters based on these features. Then, training of representative models is performed to finally choose the one with the lowest mean error per cluster. The simulation results show that by performing clustering and training the model with representative time series, it is indeed possible to obtain a low mean error while reducing the local training time per individual VM. (c) 2020 The Authors. Published by Elsevier B.V.

关键词： Clustering data Center Machine Learning Time series Prediction

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Advancing Earthquake Prediction: A Comprehensive Review of data Science Techniques 6

Advancing Earthquake Prediction: A Comprehensive Review of D...

引用

6th international conference on computing and Big data, ICCBD 2023

作者： Cornely, Pierre-Richard Wang, Jin College of Science and Mathematics Valdosta State University ValdostaGA31698 United States Valdosta State University Department of Applied Mathematics and Physics ValdostaGA31698 United States

ISBN: (纸本)9798350317008

This paper delves into the transformative potential of data science for earthquake prediction techniques. Through a thorough literature review, it explores methodologies spanning machine learning, deep learning, time series analysis, signal processing, Bayesian models, and data integration, assessing their relevance in earthquake forecasting. Key algorithms, including Random Forest, Support Vector Regression, Gradient Boosting Machine, General Linear Model, and Artificial Neural Networks, are used to interpret seismic data. Using simulation results, this study underscores the promise of these varied methods in earthquake prediction. The research concludes by pinpointing prospective areas of study and emphasizing the need for cross-disciplinary cooperation to surmount challenges in earthquake prediction, ultimately aiming to reduce the catastrophic effects of earthquakes on societies and infrastructure. © 2023 IEEE.

关键词： Prediction models

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Crime investigation using Big data on CDR 6

Crime investigation using Big Data on CDR

引用

6th IEEE international conference on Advances in Science and Technology, ICAST 2023

作者： Johri, Era Sinkar, Paritosh KJ Somaiya College of Engineering Department of Information Technology Mumbai India

ISBN: (纸本)9798350359817

In this digital era, every digital movement or change can be captured. One of the forms can be the telecommunication data and call detail records (CDR) when a person makes a call to each other. This data, after stream processing, can be analyzed on a real time basis using Spark and other technologies. This analysis can help to target the culprits behind various crimes by identifying the patterns involved in central crime figures. Great potential is associated with analyzing this data by uncovering patterns and relationships to identify suspicious callers and associated crime patterns. In this paper, we will discuss the significance of integrating cloud computing and Big data technologies in identifying criminal networks by using sophisticated algorithms. This research aims to identify the culprits behind any criminal act by analyzing the digital logs produced by any person in CDR. Identifying patterns to detect criminals by using call records holds significant potential for aiding crime investigations. This research considers 5 major telecom companies in India and with use of stream processing algorithms we can identify criminal patterns in the telecommunication records. © 2023 IEEE.

关键词： Crime

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HBC: Combining Lossy and Lossless Hybrid Bilayer Compression Framework on Time-Series data 21

HBC: Combining Lossy and Lossless Hybrid Bilayer Compression...

引用

21st IEEE international Symposium on Parallel and Distributed processing with Applications, 13th IEEE international conference on Big data and Cloud computing, 16th IEEE international conference on Social computing and Networking and 13th international conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Lu, Wanying Liu, Liang Zhai, Wenbin Chen, Haoyuan Liu, Yulei Nanjing University of Aeronautics and Astronautics China

ISBN: (纸本)9798350329223

The popularization and application of the Internet of Things (IoT) technology has brought massive time series data, which puts forward higher requirements for data compression technology. At present, most existing compression methods use only a single lossy or lossless compression algorithm to perform data compression. Furthermore, traditional lossy compression methods usually adopt a fixed error threshold. However, in practical applications, users have different accuracy requirements for time series data in different numerical ranges. In this paper, we design a Hybrid Bilayer Compression (HBC) framework, which consists of a data accuracy-aware lossy compression layer and a data feature-aware lossless compression layer. First, the original time series data is lossy compressed on the top layer of HBC, where the error threshold can be adaptively adjusted according to the user's accuracy requirements. Then, based on the features of lossy-compressed data, we use supervised learning to select the optimal lossless compression algorithm from the algorithm pool to further compress the data. Experimental results show that compared with three state-of-the-art compression algorithms, HBC reduces the storage space by 52.1%, 91.66%, and 92.27%, respectively. © 2023 IEEE.

关键词： Digital storage

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Application Research of Vertical Federated Learning Technology in Banking Risk Control Model Strategy 21

Application Research of Vertical Federated Learning Technolo...

引用

作者： Luo, Yong Lu, Zhi Yin, Xiaofei Lu, Songfeng Weng, Yiting Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan China China Minsheng Banking Corp. Ltd. Beijing China Shenzhen Huazhong University of Science and Technology Research Institute Wuhan China Shu Lun Pan Beijing China

ISBN: (纸本)9798350329223

This study centers on the application of vertical federated learning technology in the context of Internet banking loans, with a particular focus on innovations in data privacy protection, risk control model algorithms, and secure multi-party computation. Currently, banking risk control strategies mainly rely on traditional data processing technologies, which often fall short in protecting user privacy and ensuring data usage efficiency. We adopt vertical federated learning technology, offering an innovative solution for the Internet banking loan scenario. Firstly, regarding data privacy protection, we propose a differential privacy mechanism to safeguard user-sensitive data. Secondly, we innovatively apply risk control model algorithms, facilitating collaborative modeling across multiple Internet loan platforms through federated learning. Furthermore, we introduce secure multi-party computation technology to ensure the secure transmission of data and confidentiality of computation processes during federated learning. Through empirical experiments on real Internet loan datasets, we validate the effectiveness and feasibility of our proposed methods. After implementing our risk control model, the credit approval rate increased from 3.44% to 18.2%, with a single-day high reaching 25.53%. The average loan amount increased by 7,700 yuan, and the average interest rate slightly declined by 0.48%, marking a significant improvement and breakthrough compared to traditional risk control models. This study offers innovative solutions for data privacy protection and risk control in the Internet loan scenario, providing safer and more reliable services for financial institutions and users. Moreover, our methods possess high practicality and promotional value. The potential widespread impact on the industry is profound. © 2023 IEEE.

关键词： Risk management

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A Comparative study of different approaches for clustering of incomplete medical data 13

A Comparative study of different approaches for clustering o...

引用

13th international conference on Cloud computing, data Science and Engineering, Confluence 2023

作者： Goel, Sonia Tushir, Meena Maharaja Surajmal Institute of Technology Electrical and Electronics Engineering Department New Delhi India

ISBN: (纸本)9781665462631

Fuzzy c means is a conventional clustering algorithm that uses complete data sets for the clustering process, making it difficult to deal with incomplete data which is a critical problem in medical research that cannot be avoided. These missing attributes are due to several factors. A variety of imputation and non-imputation-based methods are used to estimate missing data. We reviewed various clustering algorithms used to deal with missing data in the medical sciences in this research work. The four most commonly used non-imputation and iterative clustering strategies and a variety of imputation-based FCM clustering algorithms are thoroughly examined. The Thyroid and Wisconsin breast cancer dataset naturally contain missing values from the UCI repository that are chosen for experimental results. © 2023 IEEE.

关键词： Clustering algorithms

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data Locality Aware Computation Offloading in Near Memory processing Architecture for Big data Applications 30

Data Locality Aware Computation Offloading in Near Memory Pr...

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30th international conference on High Performance computing, data, and Analytics (HiPC)

作者： Maity, Satanu Goel, Mayank Ghose, Manojit Indian Inst Informat Technol Guwahati Dept Comp Sci & Engn Gauhati India

ISBN: (纸本)9798350383225

The data-intensive applications of today's big data era often produce a large memory footprint. As a result, a significant volume of data needs to travel from memory to the CPU under the traditional Von-Neumann computing paradigm. Near-memory processing (NMP) or processing-in-memory (PIM) is a potential alternate computation framework where a computation unit is placed near the memory (or inside the memory) and a portion of an application is executed on it (termed computation offloading) aiming to reduce the amount of data movement and its consequences. Although a few computation offloading strategies have been proposed in recent times, the existing approaches do not consider the data locality offered by the last level cache and the overall execution time of the application while designing their policies. In this paper, we propose a data locality-aware computation offloading strategy for a hybrid computing system comprising the host processor and NMP-enabled 3D memory. After the application code is instrumented using the LLVM compiler framework, the strategy offloads a portion of an application to NMP if its estimated overall execution time is less. An extensive simulation performed on a set of standard simulators for a bunch of large graph-based application benchmarks reports the effectiveness of the proposed strategy by achieving a maximum speedup of 40% and 11.8% as compared to the host-only configuration and the state-of-art policy, respectively. The proposed strategy also reduces the off-chip data transfer and energy consumption by a significant margin as compared to the host-only configuration (avg 27%) and the state-of-art policy (avg 28%). Further, the proposed policy reduces the LLC miss rate by 57% as compared to the state-of-art policy.

关键词： Near memory processing Computation offloading data-intensive application Big data application

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